def test_clean_data_bmap_add_bad_3d():
# Test combination of 3d bad pixels with add_bad 3d as well
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
# N-dim list or arbitrary (len == 1 here) lists of 2-tuples
new_bad_inds = [
tuple(np.random.randint(0, high=img_dim, size=2)) for _ in range(n_im)
]
add_bad = [[ind[::-1]] for ind in new_bad_inds]
bad_cube = np.zeros((n_im, img_dim, img_dim), dtype=bool)
for i in range(n_im):
bad_cube[(i, *np.random.randint(0, high=img_dim, size=2))] = 1
data[(i, *new_bad_inds[i])] = 1e5
# Put value out of data bounds (0, 1) to make sure corrected != original
data[bad_cube] = 1e5
cleaned = clean_data(data, sky=False, apod=False, bad_map=bad_cube, add_bad=add_bad)
nobpix_list = [
fix_bad_pixels(data[i], bad_map=bad_cube[i], add_bad=add_bad[i])
for i in range(n_im)
]
full_bad_cube = bad_cube.copy()
for i in range(n_im):
full_bad_cube[(i, *new_bad_inds[i])] = True
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[full_bad_cube] != data[full_bad_cube])
assert np.all(cleaned[~full_bad_cube] == data[~full_bad_cube])
def test_clean_data_bmap_add_bad_2d():
# Test combination of bad_map and add_bad in 2d
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
new_bad_ind = tuple(np.random.randint(0, high=img_dim, size=2))
new_bad_ind_3d = (slice(None),) + new_bad_ind
add_bad = [new_bad_ind[::-1]]
bad_map = np.zeros((img_dim, img_dim), dtype=bool)
bad_map[tuple(np.random.randint(0, high=img_dim, size=2))] = 1
# Set values out of random range to test that they did change later
data[:, bad_map] = 1e5
data[new_bad_ind_3d] = 1e5
cleaned = clean_data(data, sky=False, apod=False, bad_map=bad_map, add_bad=add_bad)
nobpix_list = [
fix_bad_pixels(img, bad_map=bad_map, add_bad=add_bad) for img in data
]
full_bad_map = bad_map.copy()
full_bad_map[new_bad_ind] = True
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[:, full_bad_map] != data[:, full_bad_map])
assert np.all(cleaned[:, ~full_bad_map] == data[:, ~full_bad_map])
def test_clean_data_bmap_3d_add_bad_2d():
# Test combination of 3d bad pixels with add_bad common for all dimensions
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
new_bad_ind = tuple(np.random.randint(0, high=img_dim, size=2))
new_bad_ind_3d = (slice(None),) + new_bad_ind
add_bad = [new_bad_ind[::-1]]
bad_cube = np.zeros((n_im, img_dim, img_dim), dtype=bool)
for i in range(n_im):
bad_cube[(i, *np.random.randint(0, high=img_dim, size=2))] = 1
# Put value out of data bounds (0, 1) to make sure corrected != original
data[bad_cube] = 1e5
data[new_bad_ind_3d] = 1e5
cleaned = clean_data(data, sky=False, apod=False, bad_map=bad_cube, add_bad=add_bad)
nobpix_list = [
fix_bad_pixels(img, bad_map=bmap, add_bad=add_bad)
for img, bmap in zip(data, bad_cube)
]
full_bad_cube = bad_cube.copy()
full_bad_cube[new_bad_ind_3d] = True
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[full_bad_cube] != data[full_bad_cube])
assert np.all(cleaned[~full_bad_cube] == data[~full_bad_cube])
def test_fix_bad_pixel_no_bad():
img_dim = 80
data = np.random.random((img_dim, img_dim))
no_bpix = fix_bad_pixels(data, np.zeros_like(data, dtype=bool))
assert np.all(data == no_bpix)
def test_fix_one_bad_pixel():
img_dim = 80
data = np.random.random((img_dim, img_dim))
bad_ind = tuple(np.random.randint(0, high=img_dim, size=2))
data[bad_ind] = 1e5
bad_map = np.zeros_like(data, dtype=bool)
bad_map[bad_ind] = 1
no_bpix = fix_bad_pixels(data, bad_map)
assert no_bpix[bad_map] != data[bad_map]
assert np.all(no_bpix[~bad_map] == data[~bad_map])
assert 0.0 <= no_bpix[bad_ind] <= 1.0 # Because test data is random U(0, 1)
def test_clean_data_bmap_2d():
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
bad_map = np.zeros((img_dim, img_dim), dtype=bool)
bad_map[tuple(np.random.randint(0, high=img_dim, size=2))] = 1
data[:, bad_map] = 1e5
# Test case with non-emtpy add_bad but empty add_bad
cleaned = clean_data(data, sky=False, apod=False, bad_map=bad_map)
nobpix_list = [fix_bad_pixels(img, bad_map=bad_map) for img in data]
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[:, bad_map] != data[:, bad_map])
assert np.all(cleaned[:, ~bad_map] == data[:, ~bad_map])
def test_clean_data_bmap_3d():
# Test regular bad pixel map per-frame
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
bad_cube = np.zeros((n_im, img_dim, img_dim), dtype=bool)
for i in range(n_im):
bad_cube[(i, *np.random.randint(0, high=img_dim, size=2))] = 1
# Put value out of data bounds (0, 1) to make sure corrected != original
data[bad_cube] = 1e5
cleaned = clean_data(data, sky=False, apod=False, bad_map=bad_cube)
nobpix_list = [
fix_bad_pixels(img, bad_map=bmap) for img, bmap in zip(data, bad_cube)
]
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[bad_cube] != data[bad_cube])
assert np.all(cleaned[~bad_cube] == data[~bad_cube])
def test_clean_data_no_bmap_add_bad():
# Test add_data kwarg when no bad_map
n_im = 5
img_dim = 80
data = np.random.random((n_im, img_dim, img_dim))
bad_ind = tuple(np.random.randint(0, high=img_dim, size=2))
bad_ind_3d = (slice(None),) + bad_ind
add_bad = [bad_ind[::-1]]
# Set values out of random range to test that they did change later
data[bad_ind_3d] = 1e5
cleaned = clean_data(data, sky=False, apod=False, add_bad=add_bad)
nobpix_list = [
fix_bad_pixels(img, bad_map=np.zeros((img_dim, img_dim)), add_bad=add_bad)
for img in data
]
assert np.all([cleaned[i] == nobpix_list[i] for i in range(data.shape[0])])
assert np.all(cleaned[bad_ind_3d] != data[bad_ind_3d])